In typical social game environments, game users play games with and against other users via a network. In addition to gaming functions, many social gaming platforms provide various social networking functions that allow users to communicate with other users. For example, game users can participate in chatting with other game users in a game, share game data with other game users, and list other users as “friends” like other social networking services for enriched social networking activities.
Social media platforms such as social game platforms generally collect various types of information from users and aggregate such user information to infer relationships between users. The aggregated information and inferred relationships are often used to create recommendations or suggestions of new friends, new games, new items, and the like. Thus, the generated recommendations are provided to users for their ease of finding friends, games and/or items of their interests. Such aggregated information can be organized in the form of so-called “social graph” that represents social relationships between users. Conventionally, social media platforms leverage such “social graph” to create recommendations to their users.
Like other commercial items or contents, there are an enormous number of social game applications. Game users can download or access those game applications to play games via their electronic devices such as mobile phones, smartphones and/or other communication devices. As such, game users benefit from a recommender system that helps users effectively find games of their interests from such numerous options. Without any assistance from an automated recommender system, users would have to spend enormous time to find games that match their interests.
There are several known strategies for a content recommender system that can be implemented on social game platforms. One possible approach on a social game platform is to leverage a social graph to generate recommendation for a target game. Conventional social graph-based recommender systems generate recommendations relying on the assumption that users and their friends in close relationships in a social graph have common interests. For example, if a social graph shows that User A is a friend of User B and User B likes Game A, then a conventional recommender system utilizing the social graph provides a recommendation for the Game A to the User A. In this case, the Game A is recommended to the User A because a friend of User A (i.e., User B) likes the Game A and the recommender systems infers that the User A also likes the Game A.
However, there are limitations in the above conventional social graph-based recommender system because the relied-upon assumption that users have common interest with their real friends is not always true. As such, there is a need for a system and method for providing a recommendation for a game that match users' interests.